Technical Field
The present invention relates to object tracking, and more particularly to a surveillance system using deep network flow for multi-object tracking.
Description of the Related Art
Multi-Object Tracking (also referred to as “MOT”) is the task of predicting trajectories of all object instances in a set of images (e.g., that form a video sequence). MOT is an important computer vision problem with a wide range of applications. A predominant approach to multi-object tracking is to first find potential object instances in the video with an object detector and then to associate the bounding boxes over time to form trajectories. Many association approaches can be formulated as a linear program, including the popularly used network flows. Defining good cost functions is crucial for the success of this tracking formulation. However, previous work uses either well defined but hand-crafted functions, learns cost functions only for parts of the variables or is limited to linear cost functions.
Thus, there is a need for an improved approach to multi-object tracking.